Improving the quality of self-management support in ambulatory cancer care: a mixed-method study of organisational and clinician readiness, barriers and enablers for tailoring of implementation strategies to multisites
Bibliographic record
Abstract
INTRODUCTION: Improving the quality of self-management support (SMS) for treatment-related toxicities is a priority in cancer care. Successful implementation of SMS programmes depends on tailoring implementation strategies to organisational readiness factors and barriers/enablers, however, a systematic process for this is lacking. In this formative phase of our implementation-effectiveness trial, Self-Management and Activation to Reduce Treatment-Related Toxicities, we evaluated readiness based on constructs in the Consolidated Framework for Implementation Research (CFIR) and Normalisation Process Theory (NPT) and developed a process for mapping implementation strategies to local contexts. METHODS: In this convergent mixed-method study, surveys and interviews were used to assess readiness and barriers/enablers for SMS among stakeholders in 3 disease site groups at 3 regional cancer centres (RCCs) in Ontario, Canada. Median survey responses were classified as a barrier, enabler or neutral based on a priori cut-off values. Barriers/enablers at each centre were mapped to CFIR and then inputted into the CFIR-Expert Recommendations for Implementing Change Strategy Matching Tool V.1.0 (CFIR-ERIC) to identify centre-specific implementation strategies. Qualitative data were separately analysed and themes mapped to CFIR constructs to provide a deeper understanding of barriers/enablers. RESULTS: SMS in most of the RCCs was not systematically delivered, yet most stakeholders (n=78; respondent rate=50%) valued SMS. For centre 1, 7 barriers/12 enablers were identified, 14 barriers/9 enablers for centre 2 and 11 barriers/5 enablers for centre 3. Of the total 46 strategies identified, 30 (65%) were common across centres as core implementation strategies and 5 tailored implementation recommendations were identified for centres 1 and 3, and 4 for centre 2. CONCLUSIONS: The CFIR and CFIR-ERIC were valuable tools for tailoring SMS implementation to readiness and barriers/enablers, whereas NPT helped to clarify the clinical work of implementation. Our approach to tailoring of implementation strategies may have relevance for other studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".